- Explain Bayes Theorem? Mention few features of Bayesian Learning?
- Differentiate between Linear and Logistic Regression with an example.
- Write short notes on following:
i. Maximum a posterior
ii. Maximum Likelihood Hypothesis
iii. Genetic Algorithm
iv. Data Science Vs Machine Learning - Write the steps required for Expectation-Maximization Algorithm with the flowchart.
- Define the term hyperplane, decision boundary and also explain various types of
kernels used for Support Vector Machine. - Write the characteristics of Support Vector Machine and issues occur in Support
Vector Machine. - Here is the table given for Class_labeled training tuples from the customer database
and the given tuple is X=(Category=Youth, Income= high, Credit_Rating=excellent) and
you need to classify this tuple they pay tax or not.
CID Category Income Credit_Rating Class: Give_Tax
1 Senior low good Yes
2 Senior Very low good No
3 Youth medium excellent No
4 Middle_aged low fair Yes
5 Middle_aged Very low excellent Yes
6 Youth low good No
7 Middle_aged high fair No
8 Youth high fair No
9 Youth high excellent Yes
10 Senior high fair Yes
11 Senior low fair Yes
12 Youth low excellent Yes
13 Senior Very low good Yes
14 Middle_aged high fair No
15 Youth low good No - A patient takes a lab test and the result comes back positive. It is known that the test
returns a correct positive result in only 96% of the cases and a correct negative result
in only 95% of the cases. Furthermore, only 0.007 of the entire population has this
disease. What is the probability that this patient has cancer or not?
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